Minor edits on paper.md

docs/paper.md

@@ -93,23 +93,23 @@ bibliography: paper.bib
 # Summary
 
 How neutrons move through space, angle, energy, and time is important in modeling inertial confinement fusion systems, pulsed neutron sources, and nuclear criticality safety experiments, etc.
-This can be modeled with a Monte Carlo simulation, wherein particles with an associated statistical importance are spawned and transported to produce a particle history [@lewis_computational_1984].
+This can be modeled with a Monte Carlo simulation, wherein particles with associated statistical importance are spawned and transported to produce a particle history [@lewis_computational_1984].
 A particle's path and the specific set of events that occur within its history are governed by pseudo-random numbers, known probabilities (*e.g.*, from material data), and known geometries.
-Information about how particles move and/or interact with the system are tallied to construct a histogram solution of parameters of interest with an associated statistical error from the Monte Carlo process. 
-When simulating dynamic systems, novel numerical methods are required to performantly compute a solution.
-We designed Monte Carlo / Dynamic Code (`MC/DC`) to explore these novel numerical methods on modern high performance compute systems.
-We avoid the need of a compiled or domain specific language by using the Numba compiler for Python to accelerate and abstract our compute kernels to near compiled code speeds.
-We have implemented many novel algorithms using this scheme, and in some verification tests have approached the performance of industry-standard codes at the scale of tens of thousands of processors.
+Information about how particles move and/or interact with the system is tallied to construct a histogram solution of parameters of interest with an associated statistical error from the Monte Carlo process. 
+When simulating dynamic systems, novel numerical methods are required to compute a solution performantly.
+We designed Monte Carlo / Dynamic Code (`MC/DC`) to explore these novel numerical methods on modern high-performance compute systems.
+We avoid the need for a compiled or domain-specific language by using the Numba compiler for Python to accelerate and abstract our compute kernels to near compiled code speeds.
+We have implemented novel algorithms using this scheme and, in some verification tests, have approached the performance of industry-standard codes at the scale of tens of thousands of processors.
 
 # Statement of need
 
-`MC/DC` is a performant rapid methods development platform for novel dynamic neutron transport algorithms on modern high performance compute systems.
+`MC/DC` is a performant rapid methods development platform for novel dynamic neutron transport algorithms on modern high-performance compute systems.
 It uses the Numba compiler for Python to compile compute kernels to a desired hardware target, including support for graphics processing units (GPUs) [@lam_numba_2015].
 `MC/DC` uses `mpi4py` to gain distributed memory parallelism [@mpi4py_2021] and has run at the scale of tens of thousands of processors [@variansyah_mc23_mcdc].
-These acceleration and abstraction techniques allow `MC/DC` developers to remain in a pure Python development environment without needing to support compiled or domain specific languages.
+These acceleration and abstraction techniques allow `MC/DC` developers to remain in a pure Python development environment without needing to support compiled or domain-specific languages.
 This has allowed `MC\DC` to grow from its instantiation less than two years ago into a codebase that supports full performant neutron transport and investigation of novel transport algorithms, with development mostly from relative novices.
 
-Many of the traditionally developed neutron transport codes are export controlled (*i.e.*, are not open source and are difficult to get) and notoriously difficult to install, use, and develop in.
+Many of the traditionally developed neutron transport codes are export-controlled (*i.e.*, are not open source and are difficult to get) and notoriously difficult to install, use, and develop in.
 Because `MC/DC` is an open source and easily-installable Python package (with a `pip` distribution), it is ideal in an academic environment for both research and education.
 This is further assisted by a test suite we have developed for unit, regression, verification and performance tests, most of which run on a continuous integration basis.
 
@@ -123,7 +123,7 @@ It also supports some simple domain decomposition, with more complex algorithms
 # Future Work
 
 The main `MC/DC` branch currently only supports CPU architectures enabled by Numba (`x86-64`, `arm64`, and `ppc64`) but we are rapidly extending support to GPUs.
-We currently have operability on Nvidia GPUs (supported via Numba) and work is ongoing to enable compilation to AMD GPUs.
+We currently have operability on Nvidia GPUs (supported via Numba), and work is ongoing to enable compilation to AMD GPUs.
 On GPUs `MC/DC` will use the `harmonize` asynchronous GPU scheduler to increase performance [@brax2023].
 `harmonize` works by batching jobs during execution such that similar operations get executed at the same time, reducing the divergence between parallel threads running on the GPU at the same time.
 
@@ -133,4 +133,4 @@ We will continue to explore novel methods for dynamic neutron transport and will
 
 This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.
 
-# References
+# References